{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Deep Convolutional GAN (DC-GAN)\n",
        "\n",
        "Originally proposed by [Radford et al.](https://arxiv.org/pdf/1511.06434.pdf) is their work titled Unsupervised Representation Learning With Deep Convolutions Generative Adversarial Networks. This network uses a basic implementation where generator and discriminator models use convolutional layers, batch normalization and Upsampling. This notebook trains both networks using ADAM optimizer to play the minimax game. We showcase the effectiveness using MNIST digit generation"
      ],
      "metadata": {
        "id": "1b5uWG5fRD_d"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load Libraries"
      ],
      "metadata": {
        "id": "3CC3IcJKRLSz"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "vbDlixDVH8iQ"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import math\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "from torchvision import datasets\n",
        "from torch.autograd import Variable\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "from torchvision.utils import save_image\n",
        "import torchvision.transforms as transforms"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Check GPU"
      ],
      "metadata": {
        "id": "aK795jlgROCY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "CUDA = True if torch.cuda.is_available() else False"
      ],
      "metadata": {
        "id": "9orxI8_cIdTd"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Set Parameters"
      ],
      "metadata": {
        "id": "EiNkRrseRQqw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "NUM_CHANNELS = 1\n",
        "IMG_DIM = 32\n",
        "BATCH_SIZE = 64\n",
        "Z_DIM = 256 # Noise Vector Dimension\n",
        "N_EPOCHS = 100\n",
        "SAMPLE_INTERVAL = 400\n",
        "IMG_SHAPE = (NUM_CHANNELS,IMG_DIM, IMG_DIM)"
      ],
      "metadata": {
        "id": "-tbx0QviIdOu"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Get MNIST Dataset"
      ],
      "metadata": {
        "id": "4Lec2keHRTOi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# create directory\n",
        "os.makedirs(\"images\", exist_ok=True)\n",
        "\n",
        "# download dataset\n",
        "dataloader = torch.utils.data.DataLoader(\n",
        "    datasets.MNIST(\n",
        "        \"data/mnist\",\n",
        "        train=True,\n",
        "        download=True,\n",
        "        transform=transforms.Compose(\n",
        "            [transforms.Resize(IMG_DIM), transforms.ToTensor(), transforms.Normalize([0.5], [0.5])]\n",
        "        ),\n",
        "    ),\n",
        "    batch_size=BATCH_SIZE,\n",
        "    shuffle=True,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CbL45yEdIdL4",
        "outputId": "8a9415c4-50f2-46e7-fa86-1e655c13bb14"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to data/mnist/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 9912422/9912422 [00:00<00:00, 91008555.08it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/mnist/MNIST/raw/train-images-idx3-ubyte.gz to data/mnist/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to data/mnist/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 28881/28881 [00:00<00:00, 96599436.86it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/mnist/MNIST/raw/train-labels-idx1-ubyte.gz to data/mnist/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to data/mnist/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1648877/1648877 [00:00<00:00, 30783127.75it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/mnist/MNIST/raw/t10k-images-idx3-ubyte.gz to data/mnist/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to data/mnist/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 4542/4542 [00:00<00:00, 5945857.92it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/mnist/MNIST/raw/t10k-labels-idx1-ubyte.gz to data/mnist/MNIST/raw\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Discriminator Model"
      ],
      "metadata": {
        "id": "xyGIq53mRVzo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Discriminator(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Discriminator, self).__init__()\n",
        "\n",
        "        def discriminator_block(input_filters, output_filters, bn=True):\n",
        "            block = [\n",
        "                nn.Conv2d(input_filters, output_filters, 3, 2, 1),\n",
        "                nn.LeakyReLU(0.2, inplace=True), nn.Dropout2d(0.25),\n",
        "                nn.BatchNorm2d(output_filters, 0.8)\n",
        "                     ]\n",
        "            return block\n",
        "\n",
        "        self.model = nn.Sequential(\n",
        "            *discriminator_block(NUM_CHANNELS, 16),\n",
        "            *discriminator_block(16, 32),\n",
        "            *discriminator_block(32, 64),\n",
        "            *discriminator_block(64, 128),\n",
        "        )\n",
        "\n",
        "        # Set params for downsampled image\n",
        "        ds_size = IMG_DIM // 2 ** 4\n",
        "        self.adv_layer = nn.Sequential(nn.Linear(128 * ds_size ** 2, 1), nn.Sigmoid())\n",
        "\n",
        "    def forward(self, img):\n",
        "        out = self.model(img)\n",
        "        out = out.view(out.shape[0], -1)\n",
        "        validity = self.adv_layer(out)\n",
        "\n",
        "        return validity"
      ],
      "metadata": {
        "id": "mA5P8jByIkPh"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Generator Model"
      ],
      "metadata": {
        "id": "7pZ4AAB9RgQh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Generator(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Generator, self).__init__()\n",
        "\n",
        "        self.init_size = IMG_DIM // 4\n",
        "        self.l1 = nn.Sequential(nn.Linear(Z_DIM, 128 * self.init_size ** 2))\n",
        "\n",
        "        self.conv_blocks = nn.Sequential(\n",
        "            nn.BatchNorm2d(128),\n",
        "            nn.Upsample(scale_factor=2),\n",
        "            nn.Conv2d(128, 128, 3, stride=1, padding=1),\n",
        "            nn.BatchNorm2d(128, 0.8),\n",
        "            nn.LeakyReLU(0.2, inplace=True),\n",
        "            nn.Upsample(scale_factor=2),\n",
        "            nn.Conv2d(128, 64, 3, stride=1, padding=1),\n",
        "            nn.BatchNorm2d(64, 0.8),\n",
        "            nn.LeakyReLU(0.2, inplace=True),\n",
        "            nn.Conv2d(64, NUM_CHANNELS, 3, stride=1, padding=1),\n",
        "            nn.Tanh(),\n",
        "        )\n",
        "\n",
        "    def forward(self, z):\n",
        "        out = self.l1(z)\n",
        "        out = out.view(out.shape[0], 128, self.init_size, self.init_size)\n",
        "        img = self.conv_blocks(out)\n",
        "        return img\n"
      ],
      "metadata": {
        "id": "49kk9PlSIkMj"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Attach Loss & Optimizers"
      ],
      "metadata": {
        "id": "tXdyCr-3RiiF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize generator and discriminator\n",
        "generator = Generator()\n",
        "discriminator = Discriminator()\n",
        "\n",
        "# Loss function\n",
        "adversarial_loss = torch.nn.BCELoss()\n",
        "\n",
        "# Optimizers\n",
        "optimizer_G = torch.optim.Adam(generator.parameters(), lr=0.0002, betas=(0.5, 0.999))\n",
        "optimizer_D = torch.optim.Adam(discriminator.parameters(), lr=0.0002, betas=(0.5, 0.999))"
      ],
      "metadata": {
        "id": "OB6BKAHWKln1"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if CUDA:\n",
        "  generator.cuda()\n",
        "  discriminator.cuda()\n",
        "  adversarial_loss.cuda()\n",
        "\n",
        "  Tensor = torch.cuda.FloatTensor\n",
        "else:\n",
        "  Tensor = torch.FloatTensor"
      ],
      "metadata": {
        "id": "bnQ2grBGKllh"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train DC-GAN"
      ],
      "metadata": {
        "id": "gwYMqkcsRlD3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for epoch in range(N_EPOCHS):\n",
        "    for i, (imgs, _) in enumerate(dataloader):\n",
        "\n",
        "        # Set Real and Fake Labels\n",
        "        valid = Variable(Tensor(imgs.size(0), 1).fill_(1.0), requires_grad=False)\n",
        "        fake = Variable(Tensor(imgs.size(0), 1).fill_(0.0), requires_grad=False)\n",
        "\n",
        "        # Set Variable for real images\n",
        "        real_imgs = Variable(imgs.type(Tensor))\n",
        "\n",
        "        #  Train Generator\n",
        "        optimizer_G.zero_grad()\n",
        "\n",
        "        # Sample noise vector z for generator\n",
        "        z = Variable(Tensor(np.random.normal(0, 1, (imgs.shape[0], Z_DIM))))\n",
        "\n",
        "        # get generator output\n",
        "        gen_imgs = generator(z)\n",
        "\n",
        "        # Calculate and update generator loss\n",
        "        g_loss = adversarial_loss(discriminator(gen_imgs), valid)\n",
        "        g_loss.backward()\n",
        "        optimizer_G.step()\n",
        "\n",
        "        #  Train Discriminator\n",
        "        optimizer_D.zero_grad()\n",
        "\n",
        "        # Calculate Discriminator loss over Fake and Real Samples\n",
        "        real_loss = adversarial_loss(discriminator(real_imgs), valid)\n",
        "        fake_loss = adversarial_loss(discriminator(gen_imgs.detach()), fake)\n",
        "        d_loss = (real_loss + fake_loss) / 2\n",
        "\n",
        "        # Update Discriminator loss\n",
        "        d_loss.backward()\n",
        "        optimizer_D.step()\n",
        "        print(f'Epoch: {epoch}/{N_EPOCHS}-Batch: {i}/{len(dataloader)}--D.loss:{d_loss.item():.4f},G.loss:{g_loss.item():.4f}')\n",
        "\n",
        "        batches_done = epoch * len(dataloader) + i\n",
        "        if batches_done % SAMPLE_INTERVAL == 0:\n",
        "            save_image(gen_imgs.data[:25], f\"images/{batches_done}.png\"  , nrow=5, normalize=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "SJSKREOaKli0",
        "outputId": "187e1990-90bf-4fcb-b1ea-4cc6d89ff2ff"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-9-3ccabbd564d7>:5: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:83.)\n",
            "  valid = Variable(Tensor(imgs.size(0), 1).fill_(1.0), requires_grad=False)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "Epoch: 37/100-Batch: 551/938--D.loss:0.1575,G.loss:0.1577\n",
            "Epoch: 37/100-Batch: 552/938--D.loss:0.9384,G.loss:2.2412\n",
            "Epoch: 37/100-Batch: 553/938--D.loss:0.1065,G.loss:4.1597\n",
            "Epoch: 37/100-Batch: 554/938--D.loss:0.4071,G.loss:5.9810\n",
            "Epoch: 37/100-Batch: 555/938--D.loss:0.1373,G.loss:2.4760\n",
            "Epoch: 37/100-Batch: 556/938--D.loss:0.1125,G.loss:2.9385\n",
            "Epoch: 37/100-Batch: 557/938--D.loss:0.2515,G.loss:1.1463\n",
            "Epoch: 37/100-Batch: 558/938--D.loss:0.3536,G.loss:0.9112\n",
            "Epoch: 37/100-Batch: 559/938--D.loss:0.4207,G.loss:0.4566\n",
            "Epoch: 37/100-Batch: 560/938--D.loss:0.2358,G.loss:0.5702\n",
            "Epoch: 37/100-Batch: 561/938--D.loss:0.1209,G.loss:4.8794\n",
            "Epoch: 37/100-Batch: 562/938--D.loss:0.5767,G.loss:2.0759\n",
            "Epoch: 37/100-Batch: 563/938--D.loss:0.1736,G.loss:2.7724\n",
            "Epoch: 37/100-Batch: 564/938--D.loss:0.2890,G.loss:0.5522\n",
            "Epoch: 37/100-Batch: 565/938--D.loss:0.5926,G.loss:0.7566\n",
            "Epoch: 37/100-Batch: 566/938--D.loss:0.1875,G.loss:2.4602\n",
            "Epoch: 37/100-Batch: 567/938--D.loss:0.2344,G.loss:5.2503\n",
            "Epoch: 37/100-Batch: 568/938--D.loss:0.1676,G.loss:3.1713\n",
            "Epoch: 37/100-Batch: 569/938--D.loss:0.2142,G.loss:3.7187\n",
            "Epoch: 37/100-Batch: 570/938--D.loss:0.0511,G.loss:3.7605\n",
            "Epoch: 37/100-Batch: 571/938--D.loss:0.0792,G.loss:1.2733\n",
            "Epoch: 37/100-Batch: 572/938--D.loss:0.0248,G.loss:1.3924\n",
            "Epoch: 37/100-Batch: 573/938--D.loss:0.1608,G.loss:2.2990\n",
            "Epoch: 37/100-Batch: 574/938--D.loss:0.5079,G.loss:0.6041\n",
            "Epoch: 37/100-Batch: 575/938--D.loss:0.3490,G.loss:3.3077\n",
            "Epoch: 37/100-Batch: 576/938--D.loss:0.0878,G.loss:3.5120\n",
            "Epoch: 37/100-Batch: 577/938--D.loss:0.1131,G.loss:4.4333\n",
            "Epoch: 37/100-Batch: 578/938--D.loss:0.1844,G.loss:4.6089\n",
            "Epoch: 37/100-Batch: 579/938--D.loss:0.1482,G.loss:2.4225\n",
            "Epoch: 37/100-Batch: 580/938--D.loss:0.2266,G.loss:2.6247\n",
            "Epoch: 37/100-Batch: 581/938--D.loss:0.1436,G.loss:0.7843\n",
            "Epoch: 37/100-Batch: 582/938--D.loss:0.5708,G.loss:0.9889\n",
            "Epoch: 37/100-Batch: 583/938--D.loss:0.1718,G.loss:2.1416\n",
            "Epoch: 37/100-Batch: 584/938--D.loss:0.1128,G.loss:1.9353\n",
            "Epoch: 37/100-Batch: 585/938--D.loss:0.0635,G.loss:0.3911\n",
            "Epoch: 37/100-Batch: 586/938--D.loss:0.1006,G.loss:0.7264\n",
            "Epoch: 37/100-Batch: 587/938--D.loss:0.2832,G.loss:1.5701\n",
            "Epoch: 37/100-Batch: 588/938--D.loss:0.6098,G.loss:1.7702\n",
            "Epoch: 37/100-Batch: 589/938--D.loss:0.3496,G.loss:4.1236\n",
            "Epoch: 37/100-Batch: 590/938--D.loss:0.1115,G.loss:4.1747\n",
            "Epoch: 37/100-Batch: 591/938--D.loss:0.2844,G.loss:1.8258\n",
            "Epoch: 37/100-Batch: 592/938--D.loss:0.4445,G.loss:2.6519\n",
            "Epoch: 37/100-Batch: 593/938--D.loss:0.1005,G.loss:2.3253\n",
            "Epoch: 37/100-Batch: 594/938--D.loss:0.0953,G.loss:0.9385\n",
            "Epoch: 37/100-Batch: 595/938--D.loss:0.1753,G.loss:3.0247\n",
            "Epoch: 37/100-Batch: 596/938--D.loss:0.1741,G.loss:2.7399\n",
            "Epoch: 37/100-Batch: 597/938--D.loss:0.0698,G.loss:2.9394\n",
            "Epoch: 37/100-Batch: 598/938--D.loss:0.0726,G.loss:2.9716\n",
            "Epoch: 37/100-Batch: 599/938--D.loss:0.3365,G.loss:3.6293\n",
            "Epoch: 37/100-Batch: 600/938--D.loss:0.4697,G.loss:2.2357\n",
            "Epoch: 37/100-Batch: 601/938--D.loss:0.2650,G.loss:3.5081\n",
            "Epoch: 37/100-Batch: 602/938--D.loss:1.0311,G.loss:2.2200\n",
            "Epoch: 37/100-Batch: 603/938--D.loss:0.1161,G.loss:2.5403\n",
            "Epoch: 37/100-Batch: 604/938--D.loss:0.2421,G.loss:0.2774\n",
            "Epoch: 37/100-Batch: 605/938--D.loss:0.4483,G.loss:1.8861\n",
            "Epoch: 37/100-Batch: 606/938--D.loss:0.1751,G.loss:1.3597\n",
            "Epoch: 37/100-Batch: 607/938--D.loss:0.0963,G.loss:1.8375\n",
            "Epoch: 37/100-Batch: 608/938--D.loss:0.2118,G.loss:3.9923\n",
            "Epoch: 37/100-Batch: 609/938--D.loss:0.1959,G.loss:2.1299\n",
            "Epoch: 37/100-Batch: 610/938--D.loss:0.4349,G.loss:3.3275\n",
            "Epoch: 37/100-Batch: 611/938--D.loss:0.1808,G.loss:1.4939\n",
            "Epoch: 37/100-Batch: 612/938--D.loss:0.1018,G.loss:3.8376\n",
            "Epoch: 37/100-Batch: 613/938--D.loss:0.1702,G.loss:1.2438\n",
            "Epoch: 37/100-Batch: 614/938--D.loss:0.1536,G.loss:2.0784\n",
            "Epoch: 37/100-Batch: 615/938--D.loss:0.0773,G.loss:1.8337\n",
            "Epoch: 37/100-Batch: 616/938--D.loss:0.3233,G.loss:1.2250\n",
            "Epoch: 37/100-Batch: 617/938--D.loss:0.2766,G.loss:1.5312\n",
            "Epoch: 37/100-Batch: 618/938--D.loss:0.7473,G.loss:1.7132\n",
            "Epoch: 37/100-Batch: 619/938--D.loss:0.1639,G.loss:2.9189\n",
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          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-9-3ccabbd564d7>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0;31m# Calculate and update generator loss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0mg_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madversarial_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdiscriminator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgen_imgs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m         \u001b[0mg_loss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m         \u001b[0moptimizer_G\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/_tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m    490\u001b[0m                 \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    491\u001b[0m             )\n\u001b[0;32m--> 492\u001b[0;31m         torch.autograd.backward(\n\u001b[0m\u001b[1;32m    493\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    494\u001b[0m         )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m    249\u001b[0m     \u001b[0;31m# some Python versions print out the first line of a multi-line function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    250\u001b[0m     \u001b[0;31m# calls in the traceback and some print out the last line\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 251\u001b[0;31m     Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n\u001b[0m\u001b[1;32m    252\u001b[0m         \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    253\u001b[0m         \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5PEwwoZWL9wK"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}